code: def test_wrong_prediction_format(text_classification_dataset_mock):
      # Arrange
      emtpy_suite = Suite("Empty Suite")
  
      # Act & Assert
      assert_that(
          calling(emtpy_suite.run).with_args(
              train_dataset=text_classification_dataset_mock,
              train_predictions=[0, 0, 1, 1],
          ),
          raises(
              ValidationError,
              'Check requires predictions for the "Train" dataset '
              "to have 3 rows, same as dataset",
          ),
      )
      assert_that(
          calling(emtpy_suite.run).with_args(
              train_dataset=text_classification_dataset_mock,
              train_predictions=[[0, 1], [1, 1], [0, 0]],
          ),
          raises(
              ValidationError,
              r'Check requires predictions for the "Train" dataset to '
              r"be of a type sequence\[str\] \| sequence\[int\]",
          ),
      )
      assert_that(
          calling(emtpy_suite.run).with_args(
              train_dataset=text_classification_dataset_mock,
              train_probabilities=[[0.3, 0.5, 0.2], [0.3, 0.5, 0.2]],
          ),
          raises(
              ValidationError,
              'Check requires classification probabilities for the "Train" '
              "dataset to have 3 rows, same as dataset",
          ),
      )
      assert_that(
          calling(emtpy_suite.run).with_args(
              train_dataset=text_classification_dataset_mock,
              train_probabilities=[
                  [1, 1, 1],
                  [0, 0, 0],
                  [0.5, 0.5, 0.5],
              ],
          ),
          raises(
              ValidationError,
              'Check requires classification probabilities for the "Train" '
              "dataset to have 2 columns, same as the number of classes",
          ),
      )
      assert_that(
          calling(emtpy_suite.run).with_args(
              train_dataset=text_classification_dataset_mock,
              train_probabilities=[[1, 1], [0, 0], [0.5, 0.2]],
          ),
          raises(
              ValidationError,
              'Check requires classification probabilities for the "Train" '
              "dataset to be probabilities and sum to 1 for each row",
          ),
      )
  
      # Run with no error
      emtpy_suite.run(
          train_dataset=text_classification_dataset_mock,
          train_predictions=[1, 1, 1],
          train_probabilities=[[0.9, 0.1], [1, 0], [0.5, 0.5]],
      )
  
file_path: deepchecks/tests/nlp/test_context.py
imports: from hamcrest import assert_that, calling, close_to, raises
  from deepchecks.core.errors import ValidationError
  from deepchecks.nlp import Suite
  from deepchecks.nlp.checks import LabelDrift, SingleDatasetPerformance
label_desc: Use image node_id 1 for calling a global function with example usage: test_wrong_prediction_format(text_classification_dataset_mock) without return types